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Read More →Learners based in Belgaum can now access Uncodemy's 5–6 month instructor-led live online LLM Engineering course. The curriculum moves through LLM APIs, embeddings & vector databases, RAG, fine-tuning, LLM agents & tool calling, and deployment. Suited to developers, data scientists/ML engineers and aspiring AI engineers, the program delivers 13 modules, 100+ live sessions, 160+ hours and an enterprise LLM capstone. Fee: ₹17,500 + 18% GST, total ₹20,650. EMI starting from ₹4,032/month.
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Candidates from Belgaum who complete the Uncodemy LLM Engineering training develop working knowledge of LLM APIs, RAG pipelines, fine-tuning, vector databases and LLM agents. After wrapping up training and career preparation, eligible learners may pursue roles including LLM Engineer, AI Engineer and RAG/AI Application Engineer. Placement data for Belgaum learners is updated on a regular basis — check verified outcomes at uncodemy.com/placement.
Placed at:Cognizant
LLM engineering is the practice of designing, constructing, customising and shipping applications driven by large language models. Traditional software engineering depends on deterministic logic and data science leans on statistical prediction, but LLM engineering works in a probabilistic space with prompts, retrieval systems and model adaptation. It occupies the space where software development, machine learning and product design overlap — converting the raw power of LLMs into dependable, production-ready functionality.
What does an LLM engineer do? An LLM engineer integrates LLM APIs and SDKs, assembles RAG pipelines built on embeddings and vector databases, adapts models to specific domains, designs LLM agents that call tools, evaluates model output, and releases LLM-driven applications. The work involves Python development, prompt engineering, retrieval quality checks, API wiring, and frameworks such as LangChain that bind models, data and tools into working applications.
LLM Engineering is centred on building, adapting and shipping LLM-powered applications, while Generative AI & Agentic AI stresses autonomous agents and Data Science is about predictive modelling.
| Aspect | LLM Engineering | Generative AI | Data Science |
|---|---|---|---|
| Core focus | Building, fine-tuning & deploying LLM-powered applications | Building GenAI apps + autonomous AI agents/multi-agent systems | Predictive modelling from data |
| Coding depth | High | Moderate to High | High |
| Typical entry salary | ₹6–10 LPA (Entry-Level LLM Engineer) | [CONFIRM per GenAI course sheet] | ₹4–8 LPA (per Data Science course sheet) |
| Uncodemy program duration | 5–6 months | ~3–4 months (indicative) | 8–9 months |
Salary ranges are indicative and vary by role, skills, experience, location and employer.
The LLM Engineering course runs for 5–6 months in a live online format, with over 100 live sessions and 160+ hours of content. Its 13 modules begin with Generative AI foundations and move through Python for LLM engineering, ML & NLP fundamentals, transformer architecture, prompt engineering, LLM APIs & SDKs, embeddings & vector databases, RAG, LLM application development, fine-tuning, LLM agents & tool calling, evaluation & deployment, and a 20-hour Enterprise LLM capstone. Every module contains practical exercises using real tools, and the program finishes with hands-on projects, RAG applications and an enterprise capstone. All training is delivered live online for Belgaum and learners nationwide.
Who should join this course:
Eligibility: Familiarity with programming basics is useful but not compulsory — Python is introduced from Module 2. Some exposure to ML/NLP concepts is a bonus but will be covered in Module 3. A laptop with a dependable internet connection is essential for the practical sessions.
Find a batch slot that fits your daily routine. Live online batches run on IST. Book a free demo class first-hand, or join our upcoming weekend and weekday live batches — designed for both working professionals and students, with flexible timings and hands-on training.
| Date | Time | Trainer | Seats Left |
|---|---|---|---|
| 16oct | 07:30 PM – 08:30 PM | Mr. Irshad | 01Seat |
| 22oct | 02:00 PM – 03:00 PM | Mr. Upendra Kumar Tiwari | 02Seats |
| 26oct | 10:30 AM – 11:30 AM | Mr. Irshad | 01Seat |
| 30oct | 12:00 PM – 01:00 PM | Mr. Upendra Kumar Tiwari | 02Seats |
| Date | Time | Trainer | Seats Left |
|---|---|---|---|
| 27oct | 03:00 PM – 05:00 PM | Mr. Irshad | 02Seats |
8+ new batches start every month. All timings are in IST. Seats are limited and updated daily.
The syllabus is organised into 13 modules covering 160 hours. The order follows how LLM engineering actually works: foundations → Python → ML/NLP basics → transformer architecture → prompt engineering → LLM APIs → embeddings/vector DBs → RAG → application development → fine-tuning → agents & tool calling → evaluation/deployment → capstone. Each module includes practical exercises with real tools, and the program closes with hands-on projects, RAG applications and an enterprise LLM capstone.
| # | Module | Hours | Tools |
|---|---|---|---|
| 1 | LLM & Generative AI Foundations | 8 | ChatGPT, Gemini, Claude |
| 2 | Python for LLM Engineering | 10 | Python, Git |
| 3 | Machine Learning & NLP Fundamentals | 10 | Python, scikit-learn, NLTK |
| 4 | Transformers & LLM Architecture | 12 | Hugging Face Transformers |
| 5 | Prompt Engineering & Structured Outputs | 8 | ChatGPT, Gemini, Claude |
| 6 | Working with LLM APIs & SDKs | 10 | OpenAI/Gemini/Claude APIs, Python |
| 7 | Embeddings & Vector Databases | 10 | FAISS, Chroma, Pinecone |
| 8 | Retrieval-Augmented Generation (RAG) | 15 | LangChain, Vector DBs |
| 9 | LLM Application Development | 12 | LangChain, Python |
| 10 | Fine-Tuning & Model Adaptation | 15 | Hugging Face, LoRA |
| 11 | LLM Agents & Tool Calling | 15 | LangChain, Python |
| 12 | LLM Evaluation, Optimization & Deployment | 15 | LangSmith, Docker, Python |
| 13 | Enterprise LLM Projects & Capstone | 20 | All tools |
| Total | 160 Hours | 15+ tools | |
Once you complete all 13 modules, the assignments and the Enterprise LLM capstone project, you will receive the Uncodemy LLM Engineering Program Certificate. This document captures the program name, duration, tools covered and completion date. It is verifiable online at certificate.uncodemy.com and stands as an official record of your successful completion of the Uncodemy LLM Engineering program.
The certificate is anchored to the practical work you perform during the program — including hands-on RAG applications, fine-tuning exercises and the 20-hour Enterprise LLM capstone project. Learners work extensively with LLM APIs & SDKs (OpenAI/Gemini/Claude-style), LangChain, Vector Databases (FAISS/Chroma/Pinecone), Python, Hugging Face Transformers, prompt engineering tools, fine-tuning frameworks and evaluation/observability tools.
This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Google, Anthropic or any other LLM/vendor provider whose APIs are taught in this course. Certificate verification process pending confirmation.
Uncodemy Certificate Verification Online Certificate Verification & Download certificate.uncodemy.com_2.webp)
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LLM APIs & SDKs
LangChain
Vector Databases
Python
Hugging Face
Prompt Engineering
Fine-Tuning
Evaluation/Observability
Git & GitHub
Uncodemy is a training institute built around career outcomes, operated by Uncodemy Edutech Pvt. Ltd. With 14+ years of training experience, brick-and-mortar centres in Delhi and Noida, and live online sessions reaching students across India, Uncodemy delivers instructor-led training for emerging technologies.
This LLM Engineering program provides learners with hands-on work in RAG application development, model fine-tuning, tool-enabled LLM agents, and an enterprise LLM capstone project. It explains the workflow involved in engineering LLM-powered applications for production use, including prompt engineering, retrieval pipeline development, model adaptation, and deployment. Before enrolling, learners can read the full module-by-module curriculum.
In addition to these technical topics, Uncodemy offers instructor-led training, practical assignments, project-based learning, interview preparation, and career support. The career support policy specifies what assistance is available and its limitations, while the eligibility conditions are published before learners enrol or make payment.
If you are still deciding on a direction, Uncodemy also runs Generative AI , Data Science and Playwright Automation Testing courses for Belgaum and Karnataka learners.
Portfolio/capstone project review:Your hands-on RAG applications, fine-tuning work and enterprise LLM capstone are reviewed and refined into a portfolio an interview will find credible.
Resume & LinkedIn:We build your resume around LLM engineering keywords and measurable outcomes, and align your LinkedIn profile to match.
Interview preparation:System design for LLM applications, RAG pipeline debugging, fine-tuning trade-off questions and LLM engineering case studies specific to AI hiring.
Mock interviews:Sessions with industry trainers, followed by written feedback on what to fix.
Opportunity sharing:Relevant openings from our hiring network of 850+ companies are shared with you, with support through the application and interview process.
Continued support:This is active assistance, not a guarantee of employment — no training institute can honestly promise a job. Support continues after your program ends for learners who stay engaged with the process.
The people who actually teach this course
At Uncodemy, teaching and technical content verification are treated as two different responsibilities. Trainers are responsible for conducting live sessions, while separate domain reviewers evaluate the curriculum to ensure that the technical information remains accurate and current. For this course, Mr. Irshad reviews the curriculum and technical content, while Mr. Upendra Kumar Tiwari provides an additional review. This approach gives the course an extra layer of content validation beyond the trainer.
Every trainer and domain reviewer associated with Uncodemy is published by name, with their domain and background — you can look them up before you enrol rather than after.
Senior Data Science Trainer
Expertise15+ years of industry experience building production machine learning systems for retail, telecom and consulting teams.
Modelling security breaches in iOS apps, dormant customer prediction and factor analysis, last-mile delivery optimisation, and credit card fraud detection.
Data Analytics & AI Trainer
ExpertisePython · SQL · Machine Learning · Tableau · Artificial Intelligence · Deep Learning · Power BI · MLOps
8+ years across consulting and public-sector analytics, with deep hands-on work in AI and MLOps delivery.
Built an OCR model, a disease-prediction model, and a fraud-control model deployed across every Indian state under the Ministry of Health & Welfare.
Data Analytics & AI Trainer
ExpertiseExcel, SQL, Python, statistics, Power BI, Tableau and Generative AI
7+ years turning aviation and enterprise data into forecasting models, dashboards and AI assistants.
AI agent chat/voice bot, AI-driven data analysis, a sales-forecasting ML model, NPS data model evaluation, and a face-recognition model for biometrics.
Data Analytics Trainer
ExpertisePython · SQL · Excel · Power BI · Tableau · ML · DL · NLP · Time Series · IoT Devices · Prompt Engineering · Chatbots
5+ years in logistics and consumer-brand analytics, with a strong focus on time series, NLP and applied automation.
Speech-to-text using core NLP (T2S), talent acquisition research, and product launch & CMS work on keventers.com using Sparktech.
Data Analytics Trainer
ExpertiseAdvanced Excel · SQL · Power BI · Tableau · Python · Data Visualisation · Statistics
Trainer for the analytics and business intelligence track at Uncodemy, working with learners on Excel, SQL and BI tooling from first principles to interview-ready projects.
Guides classroom capstones on sales and customer dashboards, KPI reporting and end-to-end data cleaning workflows.
Data Analytics Trainer
Expertise
Data Analytics Trainer
Expertise
MD & CEO
ExpertiseFounder, MD & CEO of Uncodemy and GS Net Academy, with experience in education, technology leadership, research, mentorship and career development.
Leads strategic direction, academic initiatives, technology-driven education programs and mentorship across Uncodemy and GS Net Academy.
Data Analytics Trainer
Expertise
MD & CEO
ExpertiseFounder, MD & CEO of Uncodemy and GS Net Academy, with experience in education, technology leadership, research, mentorship and career development.
Leads strategic direction, academic initiatives, technology-driven education programs and mentorship across Uncodemy and GS Net Academy.
CEO & Founder
ExpertiseCEO & Founder of GvCloud Secure and Uncodemy with 14+ years of professional experience building scalable web, mobile and AI-driven solutions for clients across India, USA, UK, Canada, Australia and Singapore.
Leads technology strategy, scalable web and mobile solutions, AI-driven products and EdTech initiatives across Uncodemy and GvCloud Secure.
Belagavi is better known for foundries, precision-engineering suppliers and sugar factories than for software companies, and it is home to Visvesvaraya Technological University. Many people who look at this course here are engineers — mechanical, electrical, computer science — who can already reason about systems and want to apply that to large language models. This page is written for them: what the work involves, how it connects to local industry, how classes run from Belagavi, and where the jobs realistically are.
LLM engineering is less about clever prompts and more about building a system that behaves predictably from unpredictable parts. That is familiar ground for anyone trained to think about tolerances, failure modes and inspection. A model gives a slightly different answer each time; your job is to bound that variation, measure it and catch the bad cases before a user sees them.
What engineers from non-software branches usually lack is Python fluency and comfort with APIs, Git and deployment. The course starts Python in the second module for that reason. Programming fundamentals help, but Python is taught from Module 2 onwards and basic ML/NLP is built up in Module 3. Expect to spend extra practice time in the first two months if you have only used MATLAB or spreadsheets so far.
The following are realistic problem types in the district's industries — useful for project ideas and for understanding what a manufacturing client would ask for. They are not projects Uncodemy has delivered.
Customer specifications, inspection reports and non-conformance notes are long, formal and full of part numbers. Turning a free-text non-conformance note into a structured record with a defect code is a classic structured-output task.
Shift logs and maintenance notes are written quickly and inconsistently. A model that classifies them and pulls out machine IDs lets a supervisor search months of history.
Farmer queries about payments, cane schedules or procedures arrive in Kannada and Marathi. A carefully tested assistant that answers only from approved circulars is a fair project; a free-running chatbot is not.
Most small manufacturers here do not yet have software teams that build such tools in-house. That is why the jobs side of this page leans on remote work and bigger cities.
Belagavi sits on the Karnataka–Maharashtra border, and everyday messages mix Kannada, Marathi, Hindi and English, often typed in Roman script. That makes it a good place to learn a skill many engineers skip: measuring how a model behaves outside clean English.
This kind of written evaluation is exactly what the course's evaluation module teaches, and it reads well in an interview because it shows judgement rather than a demo.
There is no Uncodemy classroom in Karnataka; the centres are in Noida (Sector 1) and Delhi (Shakarpur Extension, Laxmi Nagar). Belagavi learners join live online batches taught by the same trainers, and can attend a free demo class before paying anything.
Build an assistant for a fictional precision-machining supplier. A quality engineer types a messy note — "bore oversize on batch 14, 3 pcs, customer drawing rev C" — and the assistant does three things.
Then evaluate it: 60–80 test notes, accuracy of each extracted field, how often the citation is right, and what the agent does with a note it cannot understand. Use invented data; never upload a real company's drawings or reports. The course's agents and tool-calling module and the hands-on projects, RAG applications and a 20-hour enterprise LLM capstone give you the structure to finish a build like this.
Few roles inside Belagavi carry an LLM title. The practical routes are remote roles with product startups, applied-AI teams in Pune and Bengaluru (both an overnight bus or train away), and the engineering-services firms that serve aerospace and manufacturing clients, some of which are starting GenAI practices.
| Route | What helps you get in | Indicative range |
|---|---|---|
| Fresher, remote or in Pune/Bengaluru | Two solid projects with evaluation, clean GitHub | ₹4–8 LPA |
| Engineer with 1–3 years in manufacturing or software | Domain knowledge plus an LLM project in that domain | ₹7–14 LPA |
| Local small firm or freelance automation work | Delivering one working tool for a nearby business | Varies widely; often project-based |
These ranges are indicative, not a promise. Actual offers depend on the company, the city and your interview; check current listings before you plan a move.
An applied-AI team building tools for factories, suppliers or engineering-services clients needs people who know what a control plan, a first-article inspection or a rework loop is. A Belagavi engineer who also understands retrieval and structured outputs can talk to those users in their own vocabulary.
On your resume, lead with the domain problem and the measured result ("extracted 6 fields from inspection notes with a tested accuracy of X% on a 70-note set"), then list the tools. Keep the numbers honest and reproducible from your repository. Uncodemy's career support covers resume reviews, mock interviews and job-application support (placement assistance, not a job guarantee).
Most interviews you take from here will be online. Expect a Python screening, a discussion of one of your projects, and LLM questions such as: how do you chunk long technical documents, why did you choose that embedding model, how would you stop the model inventing a tolerance value, and when would fine-tuning be worth it over better retrieval?
Rehearse on camera with a stable connection and a quiet room, and keep a two-minute architecture sketch of your capstone ready to screen-share. If a role asks you to relocate to Pune or Bengaluru, it is reasonable to ask whether the first months can be hybrid while you settle.
No. Uncodemy's classrooms are only in Noida (Sector 1) and Delhi (Shakarpur Extension, Laxmi Nagar). Belagavi learners join the same course through live online classes.
It is ₹17,500 + GST (₹20,650 including GST), the same as in every other city, with EMI from ₹4,032 a month (exact amount and tenure depend on the payment option).
It runs for 5–6 months: 160+ hours, 100+ live sessions and 13 modules, from Python and transformers through prompt engineering, LLM APIs, embeddings, RAG, fine-tuning, agents, evaluation and deployment.
Yes, if you are willing to learn Python properly in the first months. Your engineering habits help with evaluation and reliability; the coding is the part that needs extra time.
Many students do, but the course lasts 5–6 months, so pick a batch that avoids clashes with your exams and internship and plan for regular practice hours each week.
Not necessarily. Some roles are remote, but most dedicated LLM roles are based in larger cities, so being open to relocating or hybrid work widens your options.
Programming fundamentals help, but Python is taught from Module 2 onwards and basic ML/NLP is built up in Module 3.
No. Uncodemy offers resume reviews, mock interviews and job-application support (placement assistance, not a job guarantee). Your projects and interviews decide the outcome.
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